{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array, array_to_img\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train dataset shape: (3000, 150, 150, 3) \tValidation dataset shape: (1000, 150, 150, 3)\n"
     ]
    }
   ],
   "source": [
    "IMG_DIM = (150, 150)\n",
    "\n",
    "train_files = glob.glob('training_data/*')\n",
    "train_imgs = [img_to_array(load_img(img, target_size=IMG_DIM)) for img in train_files]\n",
    "train_imgs = np.array(train_imgs)\n",
    "train_labels = [fn.split('\\\\')[1].split('.')[0].strip() for fn in train_files]\n",
    "\n",
    "validation_files = glob.glob('validation_data/*')\n",
    "validation_imgs = [img_to_array(load_img(img, target_size=IMG_DIM)) for img in validation_files]\n",
    "validation_imgs = np.array(validation_imgs)\n",
    "validation_labels = [fn.split('\\\\')[1].split('.')[0].strip() for fn in validation_files]\n",
    "\n",
    "print('Train dataset shape:', train_imgs.shape, \n",
    "      '\\tValidation dataset shape:', validation_imgs.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_imgs_scaled = train_imgs.astype('float32')\n",
    "validation_imgs_scaled  = validation_imgs.astype('float32')\n",
    "train_imgs_scaled /= 255\n",
    "validation_imgs_scaled /= 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 150, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": 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pSkSAt170bo0LKNBdhVCICFXlFo/+t3La21iICiCfLm+n3959bhGdfuucM5aQ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\nTkSuQmCC\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=150x150 at 0x183712B2240>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(train_imgs[0].shape)\n",
    "array_to_img(train_imgs[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['cat', 'cat', 'cat', 'cat', 'cat', 'dog', 'dog', 'dog', 'dog', 'dog'] [0 0 0 0 0 1 1 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "batch_size = 30\n",
    "num_classes = 2\n",
    "epochs = 30\n",
    "input_shape = (150, 150, 3)\n",
    "\n",
    "# encode text category labels\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "le = LabelEncoder()\n",
    "le.fit(train_labels)\n",
    "train_labels_enc = le.transform(train_labels)\n",
    "validation_labels_enc = le.transform(validation_labels)\n",
    "\n",
    "print(train_labels[1495:1505], train_labels_enc[1495:1505])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 148, 148, 16)      448       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 74, 74, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 72, 72, 64)        9280      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 36, 36, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 34, 34, 128)       73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 17, 17, 128)       0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 36992)             0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 512)               18940416  \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 513       \n",
      "=================================================================\n",
      "Total params: 19,024,513\n",
      "Trainable params: 19,024,513\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n",
    "from keras.models import Sequential\n",
    "from keras import optimizers\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(16, kernel_size=(3, 3), activation='relu', \n",
    "                 input_shape=input_shape))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=optimizers.RMSprop(),\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import SVG\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "\n",
    "SVG(model_to_dot(model, show_shapes=True, \n",
    "                 show_layer_names=True, rankdir='TB').create(prog='dot', format='svg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Train on 3000 samples, validate on 1000 samples\n",
      "Epoch 1/30\n",
      "3000/3000 [==============================] - 10s - loss: 0.7583 - acc: 0.5627 - val_loss: 0.7182 - val_acc: 0.5520\n",
      "Epoch 2/30\n",
      "3000/3000 [==============================] - 8s - loss: 0.6343 - acc: 0.6533 - val_loss: 0.5891 - val_acc: 0.7190\n",
      "Epoch 3/30\n",
      "3000/3000 [==============================] - 8s - loss: 0.5274 - acc: 0.7403 - val_loss: 0.5661 - val_acc: 0.7140\n",
      "Epoch 4/30\n",
      "3000/3000 [==============================] - 8s - loss: 0.4578 - acc: 0.7957 - val_loss: 0.5587 - val_acc: 0.7350\n",
      "Epoch 5/30\n",
      "3000/3000 [==============================] - 8s - loss: 0.3531 - acc: 0.8390 - val_loss: 0.6551 - val_acc: 0.7350\n",
      "Epoch 6/30\n",
      "3000/3000 [==============================] - 8s - loss: 0.2653 - acc: 0.8830 - val_loss: 0.7276 - val_acc: 0.7310\n",
      "Epoch 7/30\n",
      "3000/3000 [==============================] - 8s - loss: 0.2216 - acc: 0.9133 - val_loss: 0.8435 - val_acc: 0.7300\n",
      "Epoch 8/30\n",
      "3000/3000 [==============================] - 8s - loss: 0.1162 - acc: 0.9547 - val_loss: 1.1065 - val_acc: 0.7160\n",
      "Epoch 9/30\n",
      "3000/3000 [==============================] - 8s - loss: 0.0913 - acc: 0.9690 - val_loss: 1.1403 - val_acc: 0.7230\n",
      "Epoch 10/30\n",
      "3000/3000 [==============================] - 8s - loss: 0.0617 - acc: 0.9820 - val_loss: 1.3576 - val_acc: 0.7250\n",
      "Epoch 11/30\n",
      "3000/3000 [==============================] - 8s - loss: 0.0523 - acc: 0.9863 - val_loss: 1.9247 - val_acc: 0.7330\n",
      "Epoch 12/30\n",
      "3000/3000 [==============================] - 8s - loss: 0.0595 - acc: 0.9840 - val_loss: 1.5851 - val_acc: 0.7210\n",
      "Epoch 13/30\n",
      "3000/3000 [==============================] - 8s - loss: 0.0397 - acc: 0.9870 - val_loss: 1.5827 - val_acc: 0.7130\n",
      "Epoch 14/30\n",
      "3000/3000 [==============================] - 8s - loss: 0.0517 - acc: 0.9883 - val_loss: 1.7607 - val_acc: 0.7250\n",
      "Epoch 15/30\n",
      "3000/3000 [==============================] - 8s - loss: 0.0402 - acc: 0.9880 - val_loss: 1.6283 - val_acc: 0.7250\n",
      "Epoch 16/30\n",
      "3000/3000 [==============================] - 8s - loss: 0.0317 - acc: 0.9907 - val_loss: 2.1635 - val_acc: 0.7120\n",
      "Epoch 17/30\n",
      "3000/3000 [==============================] - 8s - loss: 0.0542 - acc: 0.9923 - val_loss: 1.7951 - val_acc: 0.7250\n",
      "Epoch 18/30\n",
      "3000/3000 [==============================] - 8s - loss: 0.0228 - acc: 0.9933 - val_loss: 1.8231 - val_acc: 0.7090\n",
      "Epoch 19/30\n",
      "3000/3000 [==============================] - 8s - loss: 0.0253 - acc: 0.9937 - val_loss: 2.1481 - val_acc: 0.7040\n",
      "Epoch 20/30\n",
      "3000/3000 [==============================] - 8s - loss: 0.0151 - acc: 0.9947 - val_loss: 2.4051 - val_acc: 0.7100\n",
      "Epoch 21/30\n",
      "3000/3000 [==============================] - 8s - loss: 0.0372 - acc: 0.9927 - val_loss: 2.1552 - val_acc: 0.7330\n",
      "Epoch 22/30\n",
      "3000/3000 [==============================] - 8s - loss: 0.0733 - acc: 0.9887 - val_loss: 1.9452 - val_acc: 0.7300\n",
      "Epoch 23/30\n",
      "3000/3000 [==============================] - 8s - loss: 0.0527 - acc: 0.9893 - val_loss: 2.2109 - val_acc: 0.7200\n",
      "Epoch 24/30\n",
      "3000/3000 [==============================] - 8s - loss: 0.0506 - acc: 0.9937 - val_loss: 2.2416 - val_acc: 0.7110\n",
      "Epoch 25/30\n",
      "3000/3000 [==============================] - 8s - loss: 0.0640 - acc: 0.9923 - val_loss: 2.2968 - val_acc: 0.7350\n",
      "Epoch 26/30\n",
      "3000/3000 [==============================] - 8s - loss: 0.0580 - acc: 0.9893 - val_loss: 2.4733 - val_acc: 0.7000\n",
      "Epoch 27/30\n",
      "3000/3000 [==============================] - 8s - loss: 0.0492 - acc: 0.9917 - val_loss: 2.1719 - val_acc: 0.7130\n",
      "Epoch 28/30\n",
      "3000/3000 [==============================] - 8s - loss: 0.0035 - acc: 0.9990 - val_loss: 2.6527 - val_acc: 0.7240\n",
      "Epoch 29/30\n",
      "3000/3000 [==============================] - 8s - loss: 0.0314 - acc: 0.9950 - val_loss: 2.7014 - val_acc: 0.7140\n",
      "Epoch 30/30\n",
      "3000/3000 [==============================] - 8s - loss: 0.0147 - acc: 0.9967 - val_loss: 2.4963 - val_acc: 0.7220\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x=train_imgs_scaled, y=train_labels_enc,\n",
    "                    validation_data=(validation_imgs_scaled, validation_labels_enc),\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=epochs,\n",
    "                    verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Q1ty9PrYtgHbHQ5NIz8bqATZC3YAt3nqQZTvSuO3U7jYqbYwxdaBs2UfZcg9V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\nIAm1EEIIIYQQtSAJtRBCCCGEELXw/+GHyZTBbd5vAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fee2196e9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))\n",
    "t = f.suptitle('Basic CNN Performance', fontsize=12)\n",
    "f.subplots_adjust(top=0.85, wspace=0.3)\n",
    "\n",
    "epoch_list = list(range(1,31))\n",
    "ax1.plot(epoch_list, history.history['acc'], label='Train Accuracy')\n",
    "ax1.plot(epoch_list, history.history['val_acc'], label='Validation Accuracy')\n",
    "ax1.set_xticks(np.arange(0, 31, 5))\n",
    "ax1.set_ylabel('Accuracy Value')\n",
    "ax1.set_xlabel('Epoch')\n",
    "ax1.set_title('Accuracy')\n",
    "l1 = ax1.legend(loc=\"best\")\n",
    "\n",
    "ax2.plot(epoch_list, history.history['loss'], label='Train Loss')\n",
    "ax2.plot(epoch_list, history.history['val_loss'], label='Validation Loss')\n",
    "ax2.set_xticks(np.arange(0, 31, 5))\n",
    "ax2.set_ylabel('Loss Value')\n",
    "ax2.set_xlabel('Epoch')\n",
    "ax2.set_title('Loss')\n",
    "l2 = ax2.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_4 (Conv2D)            (None, 148, 148, 16)      448       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 74, 74, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 72, 72, 64)        9280      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 36, 36, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 34, 34, 128)       73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_6 (MaxPooling2 (None, 17, 17, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 15, 15, 128)       147584    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_7 (MaxPooling2 (None, 7, 7, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 6272)              0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 512)               3211776   \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 512)               262656    \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 1)                 513       \n",
      "=================================================================\n",
      "Total params: 3,706,113\n",
      "Trainable params: 3,706,113\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(16, kernel_size=(3, 3), activation='relu', \n",
    "                 input_shape=input_shape))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=optimizers.RMSprop(),\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3000 samples, validate on 1000 samples\n",
      "Epoch 1/30\n",
      "3000/3000 [==============================] - 7s - loss: 0.6945 - acc: 0.5487 - val_loss: 0.7341 - val_acc: 0.5210\n",
      "Epoch 2/30\n",
      "3000/3000 [==============================] - 7s - loss: 0.6601 - acc: 0.6047 - val_loss: 0.6308 - val_acc: 0.6480\n",
      "Epoch 3/30\n",
      "3000/3000 [==============================] - 7s - loss: 0.6327 - acc: 0.6537 - val_loss: 0.6367 - val_acc: 0.6870\n",
      "Epoch 4/30\n",
      "3000/3000 [==============================] - 7s - loss: 0.5912 - acc: 0.6853 - val_loss: 0.7449 - val_acc: 0.6320\n",
      "Epoch 5/30\n",
      "3000/3000 [==============================] - 7s - loss: 0.5632 - acc: 0.7067 - val_loss: 0.5703 - val_acc: 0.7370\n",
      "Epoch 6/30\n",
      "3000/3000 [==============================] - 7s - loss: 0.5397 - acc: 0.7433 - val_loss: 0.5375 - val_acc: 0.7360\n",
      "Epoch 7/30\n",
      "3000/3000 [==============================] - 7s - loss: 0.4706 - acc: 0.7800 - val_loss: 0.5222 - val_acc: 0.7480\n",
      "Epoch 8/30\n",
      "3000/3000 [==============================] - 7s - loss: 0.4381 - acc: 0.7897 - val_loss: 0.5248 - val_acc: 0.7460\n",
      "Epoch 9/30\n",
      "3000/3000 [==============================] - 7s - loss: 0.3877 - acc: 0.8300 - val_loss: 0.6573 - val_acc: 0.7530\n",
      "Epoch 10/30\n",
      "3000/3000 [==============================] - 7s - loss: 0.3430 - acc: 0.8613 - val_loss: 0.5613 - val_acc: 0.7610\n",
      "Epoch 11/30\n",
      "3000/3000 [==============================] - 7s - loss: 0.2805 - acc: 0.8843 - val_loss: 0.5928 - val_acc: 0.7820\n",
      "Epoch 12/30\n",
      "3000/3000 [==============================] - 7s - loss: 0.2451 - acc: 0.9007 - val_loss: 0.5285 - val_acc: 0.7680\n",
      "Epoch 13/30\n",
      "3000/3000 [==============================] - 7s - loss: 0.2194 - acc: 0.9160 - val_loss: 0.9645 - val_acc: 0.7370\n",
      "Epoch 14/30\n",
      "3000/3000 [==============================] - 7s - loss: 0.1837 - acc: 0.9277 - val_loss: 0.7820 - val_acc: 0.7360\n",
      "Epoch 15/30\n",
      "3000/3000 [==============================] - 7s - loss: 0.1600 - acc: 0.9360 - val_loss: 0.7850 - val_acc: 0.7720\n",
      "Epoch 16/30\n",
      "3000/3000 [==============================] - 7s - loss: 0.1304 - acc: 0.9530 - val_loss: 1.3080 - val_acc: 0.7580\n",
      "Epoch 17/30\n",
      "3000/3000 [==============================] - 7s - loss: 0.1253 - acc: 0.9580 - val_loss: 1.3360 - val_acc: 0.7390\n",
      "Epoch 18/30\n",
      "3000/3000 [==============================] - 7s - loss: 0.1051 - acc: 0.9633 - val_loss: 0.8860 - val_acc: 0.7820\n",
      "Epoch 19/30\n",
      "3000/3000 [==============================] - 7s - loss: 0.1108 - acc: 0.9640 - val_loss: 1.1094 - val_acc: 0.7690\n",
      "Epoch 20/30\n",
      "3000/3000 [==============================] - 7s - loss: 0.1057 - acc: 0.9667 - val_loss: 1.1956 - val_acc: 0.7730\n",
      "Epoch 21/30\n",
      "3000/3000 [==============================] - 7s - loss: 0.0953 - acc: 0.9717 - val_loss: 1.2891 - val_acc: 0.7530\n",
      "Epoch 22/30\n",
      "3000/3000 [==============================] - 7s - loss: 0.0774 - acc: 0.9740 - val_loss: 1.2380 - val_acc: 0.7860\n",
      "Epoch 23/30\n",
      "3000/3000 [==============================] - 7s - loss: 0.0785 - acc: 0.9743 - val_loss: 1.4220 - val_acc: 0.7730\n",
      "Epoch 24/30\n",
      "3000/3000 [==============================] - 7s - loss: 0.0812 - acc: 0.9733 - val_loss: 1.4394 - val_acc: 0.7720\n",
      "Epoch 25/30\n",
      "3000/3000 [==============================] - 7s - loss: 0.1255 - acc: 0.9730 - val_loss: 1.3501 - val_acc: 0.7660\n",
      "Epoch 26/30\n",
      "3000/3000 [==============================] - 7s - loss: 0.0612 - acc: 0.9810 - val_loss: 1.3949 - val_acc: 0.7830\n",
      "Epoch 27/30\n",
      "3000/3000 [==============================] - 7s - loss: 0.0771 - acc: 0.9827 - val_loss: 1.5865 - val_acc: 0.7800\n",
      "Epoch 28/30\n",
      "3000/3000 [==============================] - 7s - loss: 0.0724 - acc: 0.9743 - val_loss: 1.6714 - val_acc: 0.7790\n",
      "Epoch 29/30\n",
      "3000/3000 [==============================] - 7s - loss: 0.0927 - acc: 0.9797 - val_loss: 1.1696 - val_acc: 0.7380\n",
      "Epoch 30/30\n",
      "3000/3000 [==============================] - 7s - loss: 0.0975 - acc: 0.9803 - val_loss: 1.6790 - val_acc: 0.7840\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x=train_imgs_scaled, y=train_labels_enc,\n",
    "                    validation_data=(validation_imgs_scaled, validation_labels_enc),\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=epochs,\n",
    "                    verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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B4KWqCMqYI9KSPN0fwr0/Jq6rS6gLexz70+f3QOJ8OPtRaD/M/9er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\nIfC4MSbDVTEVSLUJ9dQ9Ifj7eDGw3dml3UoppVTl1qN5OF8/0JnQID9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5c/n6669Zvnw5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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fee1187a630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))\n",
    "t = f.suptitle('Basic CNN Performance', fontsize=12)\n",
    "f.subplots_adjust(top=0.85, wspace=0.3)\n",
    "\n",
    "epoch_list = list(range(1,31))\n",
    "ax1.plot(epoch_list, history.history['acc'], label='Train Accuracy')\n",
    "ax1.plot(epoch_list, history.history['val_acc'], label='Validation Accuracy')\n",
    "ax1.set_xticks(np.arange(0, 31, 5))\n",
    "ax1.set_ylabel('Accuracy Value')\n",
    "ax1.set_xlabel('Epoch')\n",
    "ax1.set_title('Accuracy')\n",
    "l1 = ax1.legend(loc=\"best\")\n",
    "\n",
    "ax2.plot(epoch_list, history.history['loss'], label='Train Loss')\n",
    "ax2.plot(epoch_list, history.history['val_loss'], label='Validation Loss')\n",
    "ax2.set_xticks(np.arange(0, 31, 5))\n",
    "ax2.set_ylabel('Loss Value')\n",
    "ax2.set_xlabel('Epoch')\n",
    "ax2.set_title('Loss')\n",
    "l2 = ax2.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.save('cats_dogs_basic_cnn.h5')"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
